Monitoring sitatunga (<i>Tragelaphus spekii</i>) populations using camera traps
Bibliographic record
Abstract
Abstract Population densities are an important consideration for wildlife management. For hunted species, estimates of population size are essential for establishing quotas. There are limited data regarding sitatunga (Tragelaphus spekii) abundance, and few studies have used camera traps for estimating population densities. We estimated sitatunga density in the Mayanja River area of central Uganda using the time in front of the camera (TIFC) method, which yields population density estimates using camera‐trap data without having to identify each individual. Density estimates from the TIFC method averaged 11.1 sitatunga/km2, similar to estimates from a spatially explicit capture–recapture (SECR) study, which requires perfect identification of individuals. These results suggest that TIFC methods accurately estimate population densities and might be an alternative for species where individual identity cannot be determined, or in habitats where typically identifiable species might not be accurately identified to meet the requirements of SECR. In addition, the resulting density estimates can influence management decisions and quotas for hunting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".